Due to the rapid development of deep learning, many approaches of deep domain adaptation have sprung up [1]}[2]}[3]}. Among them, many methods try to map the source domain and target domain features into a high-dimensional space, and then perform feature alignment in this space for the reduction of domain shift. The feature alignment method generally uses Maximum Mean Discrepancy (MMD) [4]} or its improved versions. Subsequently, as a result of the bloom of Generative Adversarial Networks (GAN) [5]}, the concept of adversarial learning has also been widely employed to domain adaptation.
In this way, the domain bias between the source domain and the target domain can be effectively reduced by adversarial learning.
